UAVs’ Formation Keeping Control Based on Multi–Agent System Consensus

Wang, Yu; Cheng, Zunshui; Xiao, Min

doi:10.1109/access.2020.2979996

Cited by 45 publications

(25 citation statements)

References 40 publications

(57 reference statements)

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“…The control layer controls the UAV to fly according to the planned path, which is the basis of UAV cluster research. By establishing control system frameworks [77]- [80] and designing corresponding controllers [81]- [83]], it is possible to solve the reconstruction of different types of drones in cluster formations [84]- [89], cluster search And tracking [93]- [95]and cluster anti-collision and other aspects [96]- [98]. An overall summary of the classification of the control layer is given in TABLE 4.…”

Section: Control Layermentioning

confidence: 99%

“…For the UAV formation maintenance control problem of multi-agent system consistency, based on the Routh-Hurwit stability criterion, the stability criterion of the system equilibrium point and Hopf bifurcation conditions are obtained. At the same time, the model prediction controller of the follower can predict the leader's movement state, so that the UAV formation remains stable [84]. Faced the problem of time-varying formation control of singular multi-agent systems with switched topologies, [62] designed a distributed formation controller based on output factors, through the impulse-free and equivalent exchange of singular multi-agent systems, An algorithm for solving distributed controller is designed, and the effectiveness of the method is verified by numerical simulation.…”

Section: C) Flight and Formation Control Technologymentioning

confidence: 99%

See 1 more Smart Citation

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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Section: Control Layermentioning

confidence: 99%

Section: C) Flight and Formation Control Technologymentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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“…MAS has wide-ranging applications from micro-grid control [ 14 ] to tracking control of quad-rotor [ 15 ]. In [ 16 ], a multi-agent system is used to control the formation of multiple UAVs. In [ 17 ], researchers used MAS to transport different loads using a group of aircrafts.…”

Section: Introductionmentioning

confidence: 99%

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

Ali

Han

Masood³

2021

Sensors

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This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm.

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“…The multi-agent system is specifically designed to work in hazardous environments and in very limited spaces, which are impossible for humans to reach. Multiagent systems have applications in medicine and nanotechnology [5]-magnetic [6] robots have been used to send medicine directly to human organs, planetary rovers have been used to accomplish missions on other planets [7,8], inspection robots have been used to inspect and clean the pipelines in oil and gas industries [9,10] and researchers have explored the use of unmanned aerial vehicles and swarm robot applications [11][12][13]. Due to the importance of these multi-agent systems in aiding human activities, various issues related to the cooperation and coordination of multi-agent systems have been researched and discussed up to the present day.…”

Section: Introductionmentioning

confidence: 99%

Broadcast Event-Triggered Control Scheme for Multi-Agent Rendezvous Problem in a Mixed Communication Environment

Sariff

Ismail

2021

Applied Sciences

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This paper addresses the communication issue encountered by a hybrid controller when finding consensus in terms of the rendezvous target point in a broadcast and communication environment. This issue may result in a high level of computation and the utilization of agent resources when a continuous communication is required by agents to meet convergence requirements. Thus, an event-triggered system was integrated into the design of a broadcast and distributed consensus linear controller using the simultaneous perturbation stochastic algorithm (SPSA). The agent’s movement towards the rendezvous point is based on the broadcast value, whereas the next agent’s state position depends on the distributed local controller output. The communication error obtained during communication between the agent and neighbors is only added to the gradient approximation error of the SPSA if the event-triggered function is violated. As a result, in our model, the number of channel utilizations was lower and the agents’ performances were preserved. The efficiencies and effectiveness of the proposed controller have been compared with the traditional sampling broadcast time-triggered (BTT) approach. The time and iterations required by the broadcast event-triggered (BET) system were less than 40.42% and 21% on average as compared to BTT. The trajectory was not the same—the BET showed scattered movements at the initial stage, whereas BTT showed a linear movement. In terms of the number of channels, 28.91% of channels were preserved during the few hundred iterations. Consequently, a variety of hybrid controllers with event-triggered mechanisms can be proposed for other multi-agent motion coordination tasks.

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UAVs’ Formation Keeping Control Based on Multi–Agent System Consensus

Cited by 45 publications

References 40 publications

UAV Swarm Intelligence: Recent Advances and Future Trends

UAV Swarm Intelligence: Recent Advances and Future Trends

Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

Broadcast Event-Triggered Control Scheme for Multi-Agent Rendezvous Problem in a Mixed Communication Environment

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